80 / Journal of Marketing, July 2013

The adjusted R-square for these regression equations is also healthy (at least 60%), and the instrumental variables used in each equation are significant (p < .001), suggesting that the instruments are strong.

We also perform two formal tests to evaluate the validity of our instruments. First, consistent with Bound, Jaeger, and Baker (1995), we examine the validity of our instruments by using the correlation test. The correlation matrix reported in the Web Appendix (WA6; www.marketingpower. com/ jm_webappendix) suggests that the correlations of instrumental variables with the associated endogenous variables are high, whereas those with other endogenous variables are moderate to low. Second, to ensure that our choice of instruments does not drive the directions of our results, we compare the observed average values of monetary value across the different baskets. The directions of these observed differences are similar to those from the results estimated through our simultaneous system. However, by accounting for endogeneity and simultaneity in our model, we can determine the correct magnitudes of the differences in monetary values across the baskets. Together with the theoretical arguments, these tests support the appropriate- ness of our instruments.

The instruments are exogenous or predetermined with respect to the variables studied. Nevertheless, to ensure complete independence from the variables, we estimated the model with values of the instruments from a matched sample of customers in the database. We used the propen- sity score matching method to select the matched sample, consistent with Rosenbaum and Rubin (1983). The results of this analysis, reported in the Web Appendix (WA7; www.marketingpower.com/jm_webappendix), are consistent with those reported in Table 6.

Operationalization of category characteristics as continuous variables . We test the robustness of our findings to alternative (continuous) measures of category characteristics. We generate continuous measures for each of the utilitarian, hedonic, and perceived risk characteristics for a customer’s basket by averaging the scores of each characteristic across the product categories bought. For example, if a customer purchased only apparel and acces- sories and beauty and cosmetics, his or her hedonic, utilitarian, and perceived risk scores would be 4.72, 5.88, and

4.38, respectively. The results of the monetary value model from this analysis appear in the Web Appendix (WA8; www.marketingpower.com/jm_webappendix) and are consistent with our main model results.

Alternative definitions of multichannel customer . We perform robustness checks for alternative definitions of a multichannel shopper. First, we define a multichannel shopper as someone who shops across channels but within one specific category (e.g., shoes) and across firms. The results are largely consistent with those reported in Table 6. Second, we define a multichannel shopper as someone who shops across channels and across categories but within a firm. We discuss this analysis in detail in the previous section on extension and generalization of results to the store channel (see WA5 in the Web Appendix; www.marketingpower. com/jm_webappendix). Finally, we define multichannel

9 These MAD percentage values are comparable to those Jen, Chou, and Allenby (2009) report in a similar direct marketing con-

text for predicting monetary value using linear regression.

shoppers as those who shop across channels within a cate- We also find that the perceived risk of a product category and within a firm. We use the same data as those in the

gory moderates the relationship between channel preference previous robustness check. However, we classify a customer

and monetary value for utilitarian product categories. A as a multichannel shopper if he or she purchased across

plausible rationale follows. According to RFT, for high- channels within a given product category of the multiproduct

risk/utilitarian product categories, promotion-focused cus- retailer. The results are largely consistent with those

tomers spend more in the web channel, whereas for low- reported in Table 6.

risk/utilitarian product categories, prevention-focused customers spend more in the catalog or store channel. Because of the regulatory fit of their orientation with the

product category and the channel, these customers spend We summarize our findings in Table 7. Our main effect

Implications

more in the respective channels than their other single- finding is that across product categories, an average multi-

channel or multichannel counterparts. channel customer provides higher monetary value than an

Theoretical Implications

average single-channel customer. As discussed in the “Con-

ceptual Development” section, customers who prefer multiple We contribute to the literature in several ways. First, we channels may become more engaged in the purchase process

extend prior research on the value of multichannel shoppers as they shop across channels. Greater engagement may lead

(e.g., Kumar and Venkatesan 2005) and offer new insights to more frequent purchases, a greater order quantity, greater

into the moderating effects of the product category on the spending, or a combination of all these outcomes.

channel preference–customer monetary value relationship. Importantly, our results show that product category

Contrary to conventional wisdom and prior research, we characteristics moderate the relationship between channel

show that multichannel customers are not the most valuable segment for all product categories. Our results demonstrate

preference and monetary value. The results of the two-way that traditional channel customers of low-risk/utilitarian interactions show that the positive relationship between the categories outspend multichannel customers and that web- preference for multiple channels and monetary value is only customers who buy only high-risk/utilitarian categories stronger for hedonic product categories than for utilitarian

offer higher monetary value than multichannel customers. categories. A plausible explanation is that hedonic product

Second, we extend prior research on the importance of categories are likely to evoke impulse purchase and variety-

product category characteristics on outcomes of managerial seeking behaviors, and multiple channels provide greater

relevance. Prior research has examined the importance of opportunity and convenience to engage in those behaviors.

category characteristics on variables such as unplanned pur-

A key finding is that for low-risk categories, traditional chases (Inman, Winer, and Ferraro 2009), category manage- channel customers have higher monetary value than other

ment (Dhar, Hoch, and Kumar 2001), sales promotion channel customers. This may be because low-risk product

(Ailawadi et al. 2006; Narasimhan, Neslin, and Sen 1996), categories attract prevention-focused shoppers, who pur-

revenue premium (Ailawadi, Lehmann, and Neslin 2003), chase mainly from traditional channels and spend more

and spending during economically difficult times than their electronic and multichannel counterparts.

(Kamakura and Du 2012). We extend this research by

TABLE 7 Summary of Results

Hypothesis and Finding

Retailers and Their Target Channel Segment

Strategic Question: Which Customer-Channel Segment to Target for High Monetary Value?

H 1 : M > T, E Large mass-merchandise retailers (e.g., Target, Sears) Æ multichannel customers H 2 :T U =E U =M U

Specialty retailers of utilitarian products (e.g., Best Buy, Staples) Æ all channel segments H 3 :M H >T H ,E H Specialty retailers of hedonic products (e.g., Pottery Barn, Ulta, GameStop) Æ multichannel customers

H 4 :T LR >M LR ,E LR Specialty retailers of low-risk products (e.g., Office Depot, Tractor Supply Co.) Æ traditional channel customers

H 5 :M HR =E HR =T HR Specialty retailers of high-risk products (e.g., Pier 1 Imports, Kay Jewelers) Æ all channel segments

T U–LR >E U–LR ,M U–LR Specialty retailers of low-risk/utilitarian products (e.g., PetSmart, Office Depot) Æ store-only or catalog-only customers

E U–HR >T U–HR ,M U–HR Specialty retailers of high-risk/utilitarian products (e.g., Wolf Camera, Crutchfield) Æ web-only customers

M H–LR >T H–LR ,E H–LR Specialty retailers of low-risk/hedonic products (e.g., Toys “R” Us, Ulta) Æ multichannel customers

M H–HR >T H–HR ,E H–HR Specialty retailers of high-risk/hedonic products (e.g., J.C. Penney, Pier 1 Imports) Æ multichannel customers

Notes: M = multichannel, T = traditional channel (store/catalog), and E = electronic; superscripts: H = hedonic, U = utilitarian, HR = high-risk, and LR = low-risk.

Are Multichannel Customers Really More Valuable? / 81 Are Multichannel Customers Really More Valuable? / 81

more by investing in all the channels. value.

Second, specialty retailers of hedonic products, such as Third, we illustrate the importance of the utilitarian ver-

Pottery Barn, Ulta, GameStop, and J.C. Penney could sus hedonic nature of a product category in determining the

incentivize their single-channel customers to shop in other value of shopper channel segments. The finding that web-

channels, because our findings show that multichannel only (catalog- or store-only) customers spend more than

shoppers provide the highest monetary value for such prod- multichannel customers on high-risk/(low-risk)/utilitarian

ucts. Shopping in multiple channels provides shoppers with categories suggests that the value of shopper channel seg-

more opportunities to indulge in favorable experiences ments depends on whether the category is utilitarian or

offered by hedonic products, increasing their spending on hedonic. For utilitarian categories, it is highly efficient to

those products. For example, when a web-only shopper pur- shop in a single channel and realize the best value. How-

chases a fashion clothing item on the web, a retailer such as ever, for hedonic categories, customers shopping in multi-

J.C. Penney could invite that shopper to visit its brick-and- ple channels have multiple opportunities to spend, seek

mortar store by offering a gift or a preferred item at a dis- variety, or purchase on impulse. Our findings add to the lit-

count that can be collected only at the store. When the erature on the importance of the utilitarian versus hedonic

shopper visits the store to pick up the item, he or she might nature of product categories (Chitturi, Raghunathan, and

try more hedonic products, perhaps prompting the purchase Mahajan 2008; Dhar and Wertenbroch 2000; Inman, Winer,

of more items.

and Ferraro 2009). Third, our results show that traditional channel cus- Fourth, our findings highlight the role of perceived risk

tomers of low-risk categories provide high monetary value of a product category in determining the value of shoppers

due to a strong channel–category fit in prevention focus. by their channel preference. The amount of money shoppers

Specialty retailers of low-risk products (e.g., Office Depot, spend on a product category in their preferred channel

Tractor Supply Co.) could induce traditional channel cus- depends on their perceptions of the risks associated with the

tomers to spend more at their physical stores or through category. These findings supplement prior conceptual and

their catalogs by emphasizing items that are consistent with empirical research on consumer behavior in different chan-

prevention focus. They could group similar products (e.g., nels (Balasubramanian, Raghunathan, and Mahajan 2005;

surge protectors with cables and batteries, livestock feed Van Noort, Kerkhof, and Fennis 2008; Yadav and Varadara-

with dog food and dog collar) through displays at the physi- jan 2005b).

cal stores or in catalogs to remind prevention-focused customers to buy more items on each purchase occasion.

Fifth, our results suggest important implications for the Fourth, our findings demonstrate that traditional channel interaction of perceived risk with the utilitarian nature of the customers of low-risk/utilitarian products spend more than category. The finding that a single-channel segment offers other customers. Specialty retailers of low-risk/utilitarian higher monetary value than the multichannel segment and products (e.g., Office Depot, PetSmart) could help tradi- the result that different single-channel segments provide tional channel customers routinize their shopping and pur- higher monetary values of utilitarian categories for different chase more efficiently and repeatedly at their stores or levels of risk suggest that there are some commonalities but through their catalogs. They could track the purchase histo- important differences in the underlying mechanism that

ries of these customers and prompt them to buy more of the may induce high spending. Because utilitarian categories

same items on a periodic basis.

are typically associated with a prevention focus, consumers Fifth, our findings reveal that electronic channel cus- may be emphasizing purchasing efficiency, which is better

tomers of high-risk/utilitarian products tend to outspend other realized in a single channel than in multiple channels. Con-

customers. Specialty retailers of high-risk/utilitarian products sequently, single-channel customers of utilitarian categories

(e.g., Wolf Camera, Crutchfield) could make their websites may be buying more items at higher spending levels. How-

“sticky” through features such as single-click ordering, prod- ever, at the same time, if the risk levels are high, promotion-

uct reviews, and new item recommendations. In this way, focused consumers using the web can obtain more informa-

these retailers could make it convenient for promotion- tion and buy utilitarian items more often with higher

focused customers who typically prefer the electronic chan- spending levels than those using other channels. In contrast,

nel to continue shopping and spend more in their preferred if the risk levels are low, prevention-focused consumers can

channel.

routinize their shopping and spend more on traditional Sixth, specialty retailers of high-risk/utilitarian products channels (e.g., catalog, store) than on other channels.

could also educate their prevention-focused customers who prefer to shop through catalogs or at physical stores about

80 / Journal of Marketing, July 2013